Plasmodium knowlesi infection is associated with elevated circulating biomarkers of brain injury and endothelial activation
Cescualito, RA (cesc.bertran-cobo@lshtm.ac.uk)
2024-04-15
Background
Malaria, a life-threatening disease transmitted by mosquitoes and caused by Plasmodium parasites, presents a significant global health challenge. In 2022, it accounted for an estimated 249 million cases worldwide, resulting in approximately 608,000 deaths. Within Southeast Asia, including Malaysia, malaria remains a persistent public health concern, historically attributed to Plasmodium falciparum and vivax parasites. Despite successful national efforts to eliminate indigenous malaria cases in Malaysia since 2018, the emergence of Plasmodium knowlesi (Pk), previously confined to macaque monkeys but now infecting humans, has raised new alarms.
Cerebral malaria is one of the most severe forms of malaria infection, affecting the brain and often causing coma. Survivors can suffer from brain problems even after the infection is treated. While cerebral malaria is usually linked to Plasmodium falciparum, there have been cases where other types of Plasmodium parasites also caused severe brain issues. Recent research has shown that Pk infections in Southeast Asia can also lead to severe complications, sometimes even resulting in death. Post-mortem examinations in one fatal case of severe Pk infection revealed brain damage similar to that seen in cerebral malaria caused by Plasmodium falciparum, even though Pk parasites don’t usually bind to brain blood vessels like Plasmodium falciparum does.
Our own research has found brain changes in Indian patients with severe malaria, even if they didn’t have coma. We observed higher levels of a protein associated with brain damage in these patients (S100B), along with kidney problems. This suggests that malaria can impact the brain, regardless of whether coma occurs, and kidney issues may worsen the situation. Despite similarities between cerebral malaria and severe Pk infections, and the frequent occurrence of kidney problems in severe Pk malaria, we still don’t know much about how Pk infection affects the human brain. To address this gap, we studied biomarkers related to brain injury, blood vessel activity, and inflammation in Malaysian patients infected with Pk and compared them to healthy individuals.
Methodology
Participants and samples
This study utilized stored plasma samples collected during a larger project conducted in Peninsular Malaysia from December 2019 to January 2023. Adult patients exhibiting symptoms of malaria and seeking treatment at government hospitals or private clinics across several states in Malaysia were invited to participate. Additionally, community-matched, age-matched individuals without malaria were recruited from various communities in Malaysia. These individuals were considered part of high-risk groups, such as those working in or near forests, including farmers, hunters, and natural resource collectors.
Blood samples were collected after participants provided informed consent. All data were anonymised to protect participants’ privacy. Malaria infection in each sample was confirmed using microscopic examination and molecular techniques. Out of the 50 individuals initially sampled, 38 had relevant clinical information available for this study, including parasitaemia levels, which our group has previously identified as an age-independent factor associated with severity in falciparum malaria. For this study, we analyzed de-identified samples from 19 infected patients and 19 healthy controls from Malaysia.
This study was reviewed and approved by the Medical Research and Ethics Committee of the Ministry of Health in Malaysia (22-02557-1KV) and by the Observational/Interventions Research Ethics Committee at the London School of Hygiene and Tropical Medicine, UK (27902). The samples are registered with the Human Tissue Authority, in accordance with UK national guidelines.
Statistical analysis
Load required packages
library(data.table)
library(plyr)
library(tidyverse)
library(readr)
library(readxl)
library(haven)
library(ggplot2)
library(lavaan)
library(cowplot)
library(rmarkdown)
library(gtsummary)
library(pheatmap)
library(ggpubr)
library(knitr)
library(kableExtra)
library(plotly)
library(remotes)
library(leaflet)
library(sf)
library(webshot2)
library(htmlwidgets)
library(Hmisc)Prepare datasets
# Read Data
Pk_Malaysia_database <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_database.xlsx")
Pk_Malaysia_database[, (4:5)] <- lapply(Pk_Malaysia_database[, (4:5)], as.factor)
Pk_Malaysia_database[, (7:8)] <- lapply(Pk_Malaysia_database[, (7:8)], as.factor)
Pk_Malaysia_Luminex <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_Luminex.xlsx")
Pk_Malaysia_Luminex$Dilution <- factor(Pk_Malaysia_Luminex$Dilution)
Pk_Malaysia_Luminex <- Pk_Malaysia_Luminex[,-57]
Pk_Malaysia_S100B <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_S100B.xlsx")
Pk_Malaysia_Simoa <- read_excel("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/Pk_Malaysia_Simoa.xlsx")
# Merge based on sample_ID
Pk_Malaysia_1in2 <- Pk_Malaysia_Luminex %>% filter(Dilution == "1in2") # Removing biomarkers at 1:50 dilution
Pk_Malaysia_1in2 <- Pk_Malaysia_1in2[,!names(Pk_Malaysia_1in2) %in% c("CCL18", "CCL5", "CRP", "Fetuin A", "Lipocalin 2",
"MPO", "PDGF BB", "Serpin E1", "Serpin F1", "NSE", "IL-1RA")]
Pk_Malaysia_1in2$`Ang-2/Ang-1 ratio` <- Pk_Malaysia_1in2$`Ang-2`/Pk_Malaysia_1in2$`Ang-1`
Pk_Malaysia_1in50 <- Pk_Malaysia_Luminex %>% filter(Dilution == "1in50") # Removing biomarkers at 1:2 dilution
Pk_Malaysia_1in50 <- Pk_Malaysia_1in50[,names(Pk_Malaysia_1in50) %in% c("sample_ID", "CCL18", "CCL5", "CRP", "Fetuin A",
"Lipocalin 2", "MPO", "PDGF BB", "Serpin E1", "Serpin F1",
"NSE", "IL-1RA")]
Pk_Malaysia <- merge(x = Pk_Malaysia_1in2, y = Pk_Malaysia_1in50, by = "sample_ID", all.x = TRUE)
Pk_Malaysia <- Pk_Malaysia[,-c(2:5)] # Remove unnecessary Luminex info
Pk_Malaysia <- Pk_Malaysia[,!names(Pk_Malaysia) %in% c("GFAP", "UCHL-1", "Tau total")] # Remove biomarkers re-analysied with SIMOA
Pk_Malaysia <- merge(x = Pk_Malaysia, y = Pk_Malaysia_database, by = "sample_ID", all.x = TRUE)
Pk_Malaysia <- merge(x = Pk_Malaysia, y = Pk_Malaysia_S100B, by = "sample_ID", all.x = TRUE)
Pk_Malaysia <- merge(x = Pk_Malaysia, y = Pk_Malaysia_Simoa, by = "sample_ID", all.x = TRUE)
Pk_Malaysia <- Pk_Malaysia %>% relocate(c(2:55), .after = last_col())
Pk_Malaysia_final <- Pk_Malaysia[,!names(Pk_Malaysia) %in% c("CNTF", "FGF-21", "GDNF", "NfH", "Tau ptT81")] # Remove undetectable biomarkers
Pk_Malaysia_final$S100B[is.na(Pk_Malaysia_final$S100B)] <- 27.3045 # Follow statistician's advice on S100B: Values below range (<54.6091) are replaced by half the threshold value
Pk_Malaysia_final$`Aβ(1-42)`[is.na(Pk_Malaysia_final$`Aβ(1-42)`)] <- 0.2197 # Follow statistician's advice on Aβ: Values below range (<0.4395) are replaced by half the threshold value
Pk_Malaysia_final <- Pk_Malaysia_final[,!names(Pk_Malaysia_final) %in% c("Serpin F1")] # Remove biomarkers w/o values in one of the groups
for (i in 1:length(Pk_Malaysia_final$parasitaemia)) { # Transform parasitaemia from % to parasites/uL
if (!is.na(Pk_Malaysia_final$parasitaemia[i])) {
Pk_Malaysia_final$parasitaemia[i] <- (Pk_Malaysia_final$parasitaemia[i]/100) * 5000000
}
}
# Reshape dataset for figures
Pk_Malaysia_plots <- Pk_Malaysia_final %>% gather(Biomarker, Levels, 11:63)Sample collection sites
You can hover over the map with your cursor and click the different items to visualize hospital names.
Pk_Malaysia_provinces <- st_read("/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/gadm41_MYS_shp/gadm41_MYS_1.shp")## Reading layer `gadm41_MYS_1' from data source
## `/home/cescualito/Dropbox/LABORAL/LSHTM/Wassmer Lab - Cesc Bertran-Cobo/Luminex/2023-05-09_Malaysia_samples/Analysis/Databases/gadm41_MYS_shp/gadm41_MYS_1.shp'
## using driver `ESRI Shapefile'
## Simple feature collection with 16 features and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 99.64072 ymin: 0.85372 xmax: 119.2697 ymax: 7.380556
## Geodetic CRS: WGS 84Pk_Malaysia_provinces_CTRL <- Pk_Malaysia_provinces %>%
filter(NAME_1 %in% c("Johor", "Selangor", "Negeri Sembilan", "Kedah"))
Pk_Malaysia_map <- leaflet(data) %>% # Create a leaflet map
addTiles() %>%
setView(lng = 101.9758, lat = 4.2105, zoom = 6) # Centered on Malaysia
Pk_Malaysia_map <- Pk_Malaysia_map %>% # Highlight provinces where controls were recruited
addPolygons(
data = Pk_Malaysia_provinces_CTRL,
fillColor = "yellow",
fillOpacity = 0.5,
stroke = FALSE,
popup = ~NAME_1
)
Pk_Malaysia_map_Pk_infected <- Pk_Malaysia_final %>% # Now filter out healthy control rows
filter(!is.na(latitude) & !is.na(longitude) & !is.na(NAME_1) & !is.na(hospital))
samples_per_hospital <- Pk_Malaysia_final %>% # This is to add number of samples collected per hospital
count(hospital) %>%
rename(samples_collected = n)
Pk_Malaysia_map_Pk_infected <- left_join(Pk_Malaysia_map_Pk_infected, samples_per_hospital, by = "hospital")
Pk_Malaysia_map <- Pk_Malaysia_map %>% # Add markers for hospitals with infected patients
addCircleMarkers(
data = Pk_Malaysia_map_Pk_infected,
lng = ~longitude,
lat = ~latitude,
color = "red",
radius = 5,
popup = ~paste("Hospital:", hospital, "<br>Province:", NAME_1, "<br>Samples collected:", samples_collected)
)
Pk_Malaysia_mapPk-infected patients attending either a government hospital or private clinic in Johor, Selangor, Pahang, Perak, and Trengganu states were invited to participate. Recruitment of community-matched, age-matched uninfected controls was conducted via active sample screening of individuals from communities in Johor, Selangor, Negeri Sembilan, and Kedah. Map was created in R with RStudio via Leaflet and OpenStreetMap.
Data normality
Pk_Malaysia_histograms <- ggplot(Pk_Malaysia_plots, aes(x=Levels, fill = status)) +
geom_histogram(alpha = 0.5) +
theme_classic() +
ggtitle("Histograms: Biomarker levels") +
facet_wrap(~Biomarker, scales = "free") + ylab("Count") +
scale_fill_manual(values=c("black", "red"), name = "Status")
Pk_Malaysia_histogramsPk_Malaysia_density <- ggplot(Pk_Malaysia_plots, aes(x=Levels, fill = status)) +
geom_density(aes(y=1.1*after_stat(count)), na.rm = TRUE, alpha = 0.75) +
theme_classic() +
ggtitle("Density plots: Biomarker levels") +
facet_wrap(~Biomarker, scales = "free") + ylab("Count") +
scale_fill_manual(values=c("black", "red"), name = "Status")
Pk_Malaysia_density 
Histograms (right click to
open full-size image in another tab/window)
Upon examination of histograms representing biomarker levels, it was evident that the distribution of most biomarkers deviates from normality. Given this, non-parametric statistical tests will be employed for group comparisons. By adopting this approach, we aimed to accurately assess differences in biomarker levels across teh two groups while minimizing the impact of non-normality on our analyses.
Density plots (right click
to open full-size image in another tab/window)
Similarly, upon inspection of density plots illustrating biomarker levels, it became apparent that the distribution of most biomarkers does not adhere to a normal distribution pattern.
Simple analysis of biomarker levels
Pk_Malaysia_summary <- Pk_Malaysia_plots %>%
group_by(Biomarker, status) %>%
summarise(
Participants = sum(!is.na(Levels)),
Median = format(median(Levels, na.rm = TRUE), scientific = FALSE),
IQR = format(IQR(Levels, na.rm = TRUE), scientific = FALSE)
)
Pk_Malaysia_summary_wide <- Pk_Malaysia_summary %>%
pivot_wider(names_from = status, values_from = c(Participants, Median, IQR))
names(Pk_Malaysia_summary_wide) <- c("Biomarker",
"n CTRL", "n Pk",
"Median_Control", "Median_Pk_infection",
"IQR_Control", "IQR_Pk_infection")
Pk_Malaysia_summary_wide$Median_Control <- paste(Pk_Malaysia_summary_wide$Median_Control,
"(", Pk_Malaysia_summary_wide$IQR_Control, ")", sep = " ")
Pk_Malaysia_summary_wide$Median_Pk_infection <- paste(Pk_Malaysia_summary_wide$Median_Pk_infection,
"(", Pk_Malaysia_summary_wide$IQR_Pk_infection, ")", sep = " ")
Pk_Malaysia_summary_wide <- Pk_Malaysia_summary_wide[, -c(6, 7)]
names(Pk_Malaysia_summary_wide)[names(Pk_Malaysia_summary_wide) == "Median_Control"] <- "Median (IQR) CTRL"
names(Pk_Malaysia_summary_wide)[names(Pk_Malaysia_summary_wide) == "Median_Pk_infection"] <- "Median (IQR) Pk "Brain injury biomarkers
Bonferroni-corrected analyses revealed notable differences in plasma biomarker levels between Pk-infected individuals and uninfected controls. Specifically, the Pk-infected group exhibited significantly elevated levels of brain injury biomarkers such as Tau, UCH-L1, αSyn, Park7, NRGN, and TDP-43, whereas levels of CaBD, CNTN1, NCAM-1, BDNF, GFAP, and KLK6 were significantly lower in the Pk-infected group.
Plasma levels of S100B and Aβ(1-42) were mostly undetectable in uninfected subjects, whereas the Pk-infected group showed significantly higher proportions of detectable levels for both biomarkers. Adjustments were made for individuals with undetectable levels, revealing significantly higher plasma S100B levels in the Pk-infected group after correction for multiple comparisons.
Immune activation biomarkers
Regarding biomarkers of infection and immune activation, levels of anti-inflammatory cytokines IL-1RA and IL-10, and pro-inflammatory cytokine MPO, were significantly higher in the Pk-infected group, while chemokines CCL4 and CCL5, pro-inflammatory cytokine CRP, and RAGE levels were significantly lower.
Vascular biomarkers
Endothelial activation biomarkers such as Ang-2/Ang-1 ratio and VCAM-1 levels were significantly elevated in Pk-infected patients, while Ang-1, BMP-9, PDGF-AA and -BB, and Serpin E1 levels were significantly lower.
Pk_Malaysia_final_renamed <- Pk_Malaysia_final
names(Pk_Malaysia_final_renamed) <- make.names(names(Pk_Malaysia_final_renamed)) # Ooopsies had to correct variable names with symbols such as "-" or else cannot use the function below
biomarkers <- names(Pk_Malaysia_final_renamed)[11:63]
Pk_Malaysia_Bonferroni <- purrr::map_dfr(biomarkers, function(x) {
f <- as.formula(paste0("`", x, "`", '~ status')) # Use backticks to handle column names with spaces or symbols
result <- wilcox.test(formula = f, data = Pk_Malaysia_final_renamed, exact = TRUE)
return(data.frame(name = x, p.value = result$p.value))
})
Pk_Malaysia_Bonferroni$p.adj <- p.adjust(Pk_Malaysia_Bonferroni$p,method="bonferroni")
colnames(Pk_Malaysia_Bonferroni)[2:3] <- c("P value", "Corrected P value")
Pk_Malaysia_summary_wide$Biomarker <- make.names(Pk_Malaysia_summary_wide$Biomarker)
Pk_Malaysia_final_kable <- left_join(Pk_Malaysia_summary_wide, Pk_Malaysia_Bonferroni, by = c("Biomarker" = "name"))
original_biomarker_names <- c("APP", # In the same order as they are in Pk_Malaysia_final_kable
"Ang-1",
"Ang-2",
"Ang-2/Ang-1 ratio",
"Aβ(1-42)",
"BDNF",
"BMP-9",
"CCL18",
"CCL2",
"CCL4",
"CCL5",
"CNTN1",
"CRP",
"CaBD",
"Fetuin A",
"GFAP",
"GM-CSF",
"ICAM-1",
"IFNγ",
"IL-10",
"IL-17",
"IL-1RA",
"IL-1α",
"IL-1β",
"IL-2",
"IL-4",
"IL-6",
"IL-8",
"KLK6",
"Lipocalin 2",
"MIF",
"MPO",
"NCAM-1",
"NGF-β",
"NRGN",
"NSE",
"NfL",
"OPN",
"PDGF AA",
"PDGF BB",
"Park7",
"RAGE",
"S100B",
"Serpin E1",
"TDP-43",
"TNF-α",
"Tau total",
"UCHL-1",
"VCAM-1",
"VEGF",
"YKL40",
"vWF",
"αSyn")
Pk_Malaysia_final_kable$Biomarker <- original_biomarker_names
Pk_Malaysia_final_kable <- Pk_Malaysia_final_kable %>%
kable(caption = "Biomarker median (pg/mL) and IQR, per group") %>%
kable_styling(bootstrap_options = "striped", full_width = FALSE) %>%
column_spec(column = 6, bold = Pk_Malaysia_final_kable$`P value` < 0.05) %>%
column_spec(column = 7, bold = Pk_Malaysia_final_kable$`Corrected P value` < 0.05)
Pk_Malaysia_final_kable| Biomarker | n CTRL | n Pk | Median (IQR) CTRL | Median (IQR) Pk | P value | Corrected P value |
|---|---|---|---|---|---|---|
| APP | 19 | 19 | 36081.91 ( 25944.92 ) | 17700.68 ( 12728.15 ) | 0.0009697 | 0.0513916 |
| Ang-1 | 19 | 19 | 51806.54 ( 23225.52 ) | 1547.848 ( 1633.809 ) | 0.0000000 | 0.0000000 |
| Ang-2 | 19 | 19 | 3187.404 ( 1276.748 ) | 2561.727 ( 1365.137 ) | 0.0470933 | 1.0000000 |
| Ang-2/Ang-1 ratio | 19 | 19 | 0.06218625 ( 0.0550491 ) | 1.485368 ( 0.5784377 ) | 0.0000000 | 0.0000000 |
| Aβ(1-42) | 19 | 19 | 0.2197 ( 1.15436 ) | 1.587274 ( 0.8449642 ) | 0.0769321 | 1.0000000 |
| BDNF | 19 | 19 | 1516.227 ( 1454.18 ) | 146.203 ( 115.7521 ) | 0.0000000 | 0.0000003 |
| BMP-9 | 19 | 19 | 205.9015 ( 193.2082 ) | 3.563467 ( 2.33453 ) | 0.0000008 | 0.0000424 |
| CCL18 | 19 | 19 | 76800.08 ( 37565.09 ) | 76703.89 ( 73970.48 ) | 0.8853133 | 1.0000000 |
| CCL2 | 19 | 19 | 380.842 ( 325.9286 ) | 160.88 ( 219.4045 ) | 0.0075090 | 0.3979760 |
| CCL4 | 19 | 19 | 781.81 ( 250.0735 ) | 526.2747 ( 47.64977 ) | 0.0000029 | 0.0001560 |
| CCL5 | 19 | 19 | 158730.1 ( 96050.65 ) | 17649.71 ( 27044.37 ) | 0.0000000 | 0.0000000 |
| CNTN1 | 19 | 19 | 18190.56 ( 4889.546 ) | 6179.677 ( 2615.664 ) | 0.0000000 | 0.0000000 |
| CRP | 19 | 19 | 668496.3 ( 308499 ) | 477838.2 ( 235913.7 ) | 0.0005433 | 0.0287954 |
| CaBD | 19 | 19 | 1807.297 ( 178.3879 ) | 1151.089 ( 220.9344 ) | 0.0000000 | 0.0000000 |
| Fetuin A | 19 | 19 | 176390803 ( 38746703 ) | 154460943 ( 47209645 ) | 0.0462397 | 1.0000000 |
| GFAP | 19 | 18 | 78.90895 ( 45.83352 ) | 29.85289 ( 25.34285 ) | 0.0000256 | 0.0013582 |
| GM-CSF | 19 | 19 | 23.77626 ( 10.0321 ) | 21.52029 ( 8.598411 ) | 0.2423497 | 1.0000000 |
| ICAM-1 | 19 | 19 | 705781.7 ( 710687.9 ) | 832536.7 ( 324396.6 ) | 0.9769881 | 1.0000000 |
| IFNγ | 19 | 19 | 101.4179 ( 11.8365 ) | 104.8003 ( 46.26716 ) | 0.7253783 | 1.0000000 |
| IL-10 | 19 | 19 | 7.995004 ( 1.332426 ) | 48.62692 ( 115.9813 ) | 0.0000001 | 0.0000076 |
| IL-17 | 19 | 19 | 29.19267 ( 6.138356 ) | 22.18039 ( 8.324175 ) | 0.0080604 | 0.4272030 |
| IL-1RA | 19 | 19 | 6736.133 ( 2506.003 ) | 35639.06 ( 21569.21 ) | 0.0000000 | 0.0000000 |
| IL-1α | 19 | 19 | 49.0339 ( 46.40482 ) | 46.03038 ( 21.17682 ) | 0.2931215 | 1.0000000 |
| IL-1β | 19 | 19 | 24.66162 ( 7.188521 ) | 40.03614 ( 17.9049 ) | 0.0027363 | 0.1450245 |
| IL-2 | 19 | 19 | 34.81916 ( 28.69849 ) | 33.1313 ( 62.47699 ) | 0.5291252 | 1.0000000 |
| IL-4 | 19 | 19 | 165.066 ( 12.23509 ) | 159.8323 ( 35.17321 ) | 0.4383034 | 1.0000000 |
| IL-6 | 19 | 18 | 11.48631 ( 2.631257 ) | 21.7437 ( 19.21264 ) | 0.0012495 | 0.0662253 |
| IL-8 | 19 | 19 | 319.6725 ( 598.992 ) | 254.5794 ( 2353.187 ) | 0.8174633 | 1.0000000 |
| KLK6 | 19 | 19 | 2319.536 ( 915.0541 ) | 1072.432 ( 788.647 ) | 0.0002475 | 0.0131159 |
| Lipocalin 2 | 19 | 19 | 547607.5 ( 149670.6 ) | 619695.2 ( 94134.2 ) | 0.0496557 | 1.0000000 |
| MIF | 19 | 19 | 384.5975 ( 315.7701 ) | 630.6708 ( 321.2088 ) | 0.0022218 | 0.1177543 |
| MPO | 19 | 19 | 1571073 ( 869998.1 ) | 3176104 ( 919811.7 ) | 0.0006159 | 0.0326436 |
| NCAM-1 | 19 | 19 | 107813.5 ( 38203.57 ) | 70203.28 ( 8851.253 ) | 0.0000016 | 0.0000833 |
| NGF-β | 17 | 19 | 0.9257768 ( 1.199232 ) | 1.731491 ( 1.480931 ) | 0.0203291 | 1.0000000 |
| NRGN | 18 | 19 | 22.3997 ( 11.08561 ) | 241.3741 ( 763.047 ) | 0.0000427 | 0.0022606 |
| NSE | 19 | 16 | 54323.95 ( 21601.19 ) | 1094366 ( 1382298 ) | 0.0009665 | 0.0512258 |
| NfL | 19 | 19 | 8.139866 ( 4.438695 ) | 6.271222 ( 5.765971 ) | 0.2120048 | 1.0000000 |
| OPN | 19 | 19 | 12980.99 ( 11051.35 ) | 5056.864 ( 3291.073 ) | 0.0033420 | 0.1771263 |
| PDGF AA | 19 | 19 | 3864.416 ( 688.8593 ) | 552.6219 ( 346.1488 ) | 0.0000000 | 0.0000000 |
| PDGF BB | 19 | 18 | 18494.51 ( 7645.818 ) | 1385.736 ( 2055.839 ) | 0.0000004 | 0.0000190 |
| Park7 | 19 | 19 | 71436.38 ( 61371.81 ) | 161810.2 ( 146171 ) | 0.0000109 | 0.0005799 |
| RAGE | 19 | 19 | 7205.683 ( 2599.431 ) | 4796.721 ( 1839.571 ) | 0.0000495 | 0.0026236 |
| S100B | 19 | 19 | 27.3045 ( 0 ) | 1282.191 ( 1777.194 ) | 0.0000001 | 0.0000043 |
| Serpin E1 | 18 | 19 | 932450 ( 468183.9 ) | 183488.2 ( 103411 ) | 0.0000000 | 0.0000000 |
| TDP-43 | 17 | 19 | 5344.277 ( 5805.93 ) | 67142.7 ( 90407.26 ) | 0.0000971 | 0.0051444 |
| TNF-α | 19 | 19 | 16.22958 ( 5.688821 ) | 13.71272 ( 2.301574 ) | 0.0392982 | 1.0000000 |
| Tau total | 19 | 19 | 0.5577688 ( 0.57288 ) | 3.685658 ( 4.909908 ) | 0.0000131 | 0.0006926 |
| UCHL-1 | 19 | 19 | 49.30495 ( 45.35367 ) | 336.6019 ( 184.0623 ) | 0.0000004 | 0.0000219 |
| VCAM-1 | 19 | 19 | 698022.3 ( 419633.4 ) | 1673430 ( 2154223 ) | 0.0000024 | 0.0001261 |
| VEGF | 19 | 19 | 147.2947 ( 81.1504 ) | 178.8963 ( 118.7221 ) | 0.2548461 | 1.0000000 |
| YKL40 | 19 | 19 | 29591.46 ( 24732.27 ) | 58673.75 ( 33186.66 ) | 0.0019910 | 0.1055228 |
| vWF | 19 | 19 | 6716.777 ( 8770.726 ) | 6173.033 ( 3737.156 ) | 0.2014334 | 1.0000000 |
| αSyn | 19 | 19 | 863.401 ( 259.8839 ) | 2177.755 ( 392.3615 ) | 0.0000004 | 0.0000219 |
Biomarkers of brain injury:
Bonferroni-corrected group comparisons (right click to open
full-size image in another tab/window)
Hierarchical clustering heatmaps
Brain biomarker subset
Pk_Malaysia_final_brain_subset_list <- c(
"αSyn", "APP", "Aβ(1-42)", "BDNF", "CaBD", "CNTN1", "NSE",
"Fetuin A", "GFAP", "KLK6", "NCAM-1", "Lipocalin 2", "NGF-β", "NfL", "NRGN",
"Park7", "S100B", "TDP-43", "Tau total", "UCHL-1", "YKL40"
)
Pk_Malaysia_final_brain_subset <- Pk_Malaysia_final %>% select(Participants, Pk_Malaysia_final_brain_subset_list)
row.names(Pk_Malaysia_final_brain_subset) <- Pk_Malaysia_final_brain_subset$Participants
Pk_Malaysia_final_brain_subset$Participants <- NULL
Pk_Malaysia_final_brain_subset[is.na(Pk_Malaysia_final_brain_subset)] <- 0 # Replace NAs with zeros
Pk_Malaysia_final_brain_subset <- scale(Pk_Malaysia_final_brain_subset) # Scale data
Pk_Malaysia_final_brain_subset_dist <- dist(Pk_Malaysia_final_brain_subset) # Base R code
Pk_Malaysia_final_brain_subset_hclust <- hclust(Pk_Malaysia_final_brain_subset_dist)
plot(Pk_Malaysia_final_brain_subset_hclust)pheatmap(Pk_Malaysia_final_brain_subset, cutree_rows = 2, cutree_cols = 2, scale = "none")
Biomarkers of brain injury: Hierarchical
clustering heatmap (right click to open full-size image in
another tab/window)
Hierarchical clustering heatmap analysis unveiled distinct group patterns for brain injury biomarkers, delineating cohesive clusters predominantly composed of infected individuals with elevated levels of S100B, Park7, αSyn, and TDP-43. Notably, two infected individuals displayed atypical clustering with the healthy controls, indicating a subgroup with a unique biomarker profile. Conversely, in the control group, certain biomarkers including BDNF, CaBD, CNTN1, and GFAP formed cohesive clusters with higher levels, representing a baseline biomarker profile in healthy individuals. However, two infected individuals clustered with the control group in this category, suggesting potential overlap or similarity in the levels of these specific biomarkers between infected and control individuals.
Immune biomarker subset
Pk_Malaysia_final_immune_subset_list <- c(
"CRP", "GM-CSF", "IFNγ", "IL-1α", "IL-1β", "IL-2", "IL-6", "IL-8",
"IL-17", "MIF", "MPO", "TNF-α", "IL-1RA", "IL-4", "IL-10",
"CCL2", "CCL4", "CCL5", "CCL18", "OPN", "RAGE"
)
Pk_Malaysia_final_immune_subset <- Pk_Malaysia_final %>% select(Participants, Pk_Malaysia_final_immune_subset_list)
row.names(Pk_Malaysia_final_immune_subset) <- Pk_Malaysia_final_immune_subset$Participants
Pk_Malaysia_final_immune_subset$Participants <- NULL
Pk_Malaysia_final_immune_subset[is.na(Pk_Malaysia_final_immune_subset)] <- 0 # Replace NAs with zeros
Pk_Malaysia_final_immune_subset <- scale(Pk_Malaysia_final_immune_subset) # Scale data
Pk_Malaysia_final_immune_subset_dist <- dist(Pk_Malaysia_final_immune_subset) # Base R code
Pk_Malaysia_final_immune_subset_hclust <- hclust(Pk_Malaysia_final_immune_subset_dist)
plot(Pk_Malaysia_final_immune_subset_hclust)pheatmap(Pk_Malaysia_final_immune_subset, scale = "none")
Biomarkers of infection and immunity:
Hierarchical clustering heatmap (right click to open
full-size image in another tab/window)
Clustering analysis of biomarkers associated with infection and immune activation did not reveal distinct separation between the two groups. Higher levels of IL-1RA and MPO predominantly clustered in the infected group, with two control individuals exhibiting similar patterns. Additionally, one infected individual displayed notably high levels of IL-1β, GM-CSF, TNF-α, CCL4, and CCL2, while another exhibited elevated levels of OPN and IL-10. Furthermore, one control individual showed high levels of IL-6.
Vascular biomarker subset
Pk_Malaysia_final_vascular_subset_list <- c(
"Ang-1", "Ang-2", "Ang-2/Ang-1 ratio", "BMP-9", "ICAM-1",
"PDGF AA", "PDGF BB", "Serpin E1", "VCAM-1", "VEGF", "vWF"
)
Pk_Malaysia_final_vascular_subset <- Pk_Malaysia_final %>% select(Participants, Pk_Malaysia_final_vascular_subset_list)
row.names(Pk_Malaysia_final_vascular_subset) <- Pk_Malaysia_final_vascular_subset$Participants
Pk_Malaysia_final_vascular_subset$Participants <- NULL
Pk_Malaysia_final_vascular_subset[is.na(Pk_Malaysia_final_vascular_subset)] <- 0 # Replace NAs with zeros
Pk_Malaysia_final_vascular_subset <- scale(Pk_Malaysia_final_vascular_subset) # Scale data
Pk_Malaysia_final_vascular_subset_dist <- dist(Pk_Malaysia_final_vascular_subset) # Base R code
Pk_Malaysia_final_vascular_subset_hclust <- hclust(Pk_Malaysia_final_vascular_subset_dist)
plot(Pk_Malaysia_final_vascular_subset_hclust)pheatmap(Pk_Malaysia_final_vascular_subset, cutree_rows = 2, cutree_cols = 2, scale = "none")
Vascular biomarkers: Hierarchical
clustering heatmap (right click to open full-size image in
another tab/window)
Lastly, clustering of vascular biomarkers exhibited clear and distinctive group profiles. In the control group, a cohesive cluster characterized by high levels of PDGF-AA, Ang-1, PDGF-BB, Serpin E1, and BMP-9 was observed. Conversely, in the infected group, a distinct cluster with higher levels of VCAM-1 and the Ang-2/Ang-1 ratio was identified.
Biomarker correlation with age
Pk_Malaysia_age <- Pk_Malaysia_plots %>%
ggplot(aes(y = Levels, x = age, color=status, group = sample_ID)) +
geom_point(size = 1.5) +
geom_smooth(method = lm, formula = y ~ x, se = FALSE) +
xlab("Age") + ylab("pg/mL") +
theme_classic() +
ggtitle("") +
facet_wrap(~Biomarker, scales = "free") +
scale_color_manual(values=c("black", "red"), name = "Status") +
stat_cor(method = "spearman") +
geom_smooth(method = lm, formula = y ~ x, se = FALSE)
Pk_Malaysia_age
Biomarker correlation with age
(right click to open full-size image in another
tab/window)
No significant correlations were identified between the levels of any of the examined biomarkers and the age of participants within each respective group.
Biomarker correlation with parasitaemia
Pk_Malaysia_par <- Pk_Malaysia_plots %>%
ggplot(aes(y = Levels, x = parasitaemia, color=status, group = sample_ID)) +
geom_point(size = 1.5) +
geom_smooth(method = lm, formula = y ~ x, se = FALSE) +
xlab("Parasites/μL") + ylab("pg/mL") +
theme_classic() +
ggtitle("") +
facet_wrap(~Biomarker, scales = "free") +
scale_color_manual(values=c("black", "red"), name = "Status") +
stat_cor(method = "spearman") +
geom_smooth(method = lm, formula = y ~ x, se = FALSE) +
scale_x_continuous(labels = scales::scientific_format(digits = 1, decimal.mark = ",", big.mark = "x10^")) +
scale_y_continuous(labels = scales::scientific_format(digits = 1, decimal.mark = ",", big.mark = "x10^"))
Pk_Malaysia_par
Biomarker correlation with
parasitaemia (right click to open full-size image in
another tab/window)
Furthermore, within the Pk-infected group, no significant correlations were observed between biomarker levels and the percentage of parasitaemia.
Codebook
Pk_Malaysia_final_codebook <- describe(Pk_Malaysia_final[, -which(names(Pk_Malaysia_final) == "sample_ID")])
Pk_Malaysia_final_codebook## Pk_Malaysia_final[, -which(names(Pk_Malaysia_final) == "sample_ID")]
##
## 62 Variables 38 Observations
## --------------------------------------------------------------------------------
## Participants
## n missing distinct
## 38 0 38
##
## lowest : Control 1 Control 10 Control 11 Control 12 Control 13
## highest: Pk infected 5 Pk infected 6 Pk infected 7 Pk infected 8 Pk infected 9
## --------------------------------------------------------------------------------
## age
## n missing distinct Info Mean Gmd .05 .10
## 36 2 20 0.994 38.5 14.81 26.0 27.5
## .25 .50 .75 .90 .95
## 29.0 32.0 44.5 61.0 66.5
##
## Value 24.00 25.65 26.75 27.85 28.95 29.50 30.60 31.70 32.80 35.00 37.75
## Frequency 1 2 1 3 3 2 5 4 3 1 1
## Proportion 0.028 0.056 0.028 0.083 0.083 0.056 0.139 0.111 0.083 0.028 0.028
##
## Value 43.80 46.00 53.70 54.80 57.00 58.65 62.50 76.80 79.00
## Frequency 1 1 1 1 1 1 2 1 1
## Proportion 0.028 0.028 0.028 0.028 0.028 0.028 0.056 0.028 0.028
##
## For the frequency table, variable is rounded to the nearest 0.55
## --------------------------------------------------------------------------------
## sex
## n missing distinct value
## 38 0 1 Male
##
## Value Male
## Frequency 38
## Proportion 1
## --------------------------------------------------------------------------------
## status
## n missing distinct
## 38 0 2
##
## Value Control Pk infection
## Frequency 19 19
## Proportion 0.5 0.5
## --------------------------------------------------------------------------------
## parasitaemia
## n missing distinct Info Mean Gmd .05 .10
## 19 19 19 1 52402 82072 977 1168
## .25 .50 .75 .90 .95
## 3728 15200 30474 123520 248366
##
## Value 410.00 9178.94 13563.41 17947.88 22332.35 26716.82
## Frequency 7 2 1 2 1 1
## Proportion 0.368 0.105 0.053 0.105 0.053 0.053
##
## Value 31101.29 39870.23 96868.34 224017.97 438857.00
## Frequency 1 1 1 1 1
## Proportion 0.053 0.053 0.053 0.053 0.053
##
## For the frequency table, variable is rounded to the nearest 4384.47
## --------------------------------------------------------------------------------
## hospital
## n missing distinct
## 19 19 8
##
## lowest : Hospital Kota Tinggi Hospital Kuala Kubu Bharu Hospital Kuala Lipis Hospital Mersing Hospital Setiu
## highest: Hospital Mersing Hospital Setiu Hospital Sungai Siput Klinik Kesihatan Betau Pejabat Kesihatan Daerah Kota Tinggi
## --------------------------------------------------------------------------------
## NAME_1
## n missing distinct
## 19 19 5
##
## Value Johor Pahang Perak Selangor Trengganu
## Frequency 3 5 2 8 1
## Proportion 0.158 0.263 0.105 0.421 0.053
## --------------------------------------------------------------------------------
## latitude
## n missing distinct Info Mean Gmd
## 19 19 8 0.917 3.741 1.057
##
## Value 1.738177 2.406427 3.546384 4.175325 4.253943 4.843576 5.669062
## Frequency 2 1 8 4 1 2 1
## Proportion 0.105 0.053 0.421 0.211 0.053 0.105 0.053
##
## For the frequency table, variable is rounded to the nearest 0.03930885
## --------------------------------------------------------------------------------
## longitude
## n missing distinct Info Mean Gmd
## 19 19 8 0.917 102.1 0.8879
##
## Value 101.0626 101.6319 101.6888 102.0304 102.7420 103.8237 103.8806
## Frequency 2 8 1 4 1 1 1
## Proportion 0.105 0.421 0.053 0.211 0.053 0.053 0.053
##
## Value 103.9091
## Frequency 1
## Proportion 0.053
##
## For the frequency table, variable is rounded to the nearest 0.02846519
## --------------------------------------------------------------------------------
## S100B
## n missing distinct Info Mean Gmd .05 .10
## 38 0 24 0.939 798.8 1149 26.13 27.30
## .25 .50 .75 .90 .95
## 27.30 97.44 1252.67 2596.85 2742.90
##
## Value 0 50 100 150 250 500 550 1050 1150 1250 1450
## Frequency 18 1 1 2 1 1 1 2 1 1 3
## Proportion 0.474 0.026 0.026 0.053 0.026 0.026 0.026 0.053 0.026 0.026 0.079
##
## Value 2050 2550 2650 3000 5850
## Frequency 1 1 2 1 1
## Proportion 0.026 0.026 0.053 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 50
## --------------------------------------------------------------------------------
## GFAP
## n missing distinct Info Mean Gmd .05 .10
## 37 1 37 1 73.99 65.14 16.93 19.41
## .25 .50 .75 .90 .95
## 28.79 57.64 93.56 119.51 161.18
##
## lowest : 12.5061 14.8444 17.4455 17.637 20.5881
## highest: 119.118 120.086 137.167 257.212 398.535
## --------------------------------------------------------------------------------
## NfL
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 9.799 7.194 3.402 4.478
## .25 .50 .75 .90 .95
## 5.185 7.431 10.872 20.797 24.833
##
## lowest : 1.97673 2.53153 3.55605 4.38567 4.51757
## highest: 20.7435 20.9222 24.4438 27.0413 37.6341
## --------------------------------------------------------------------------------
## Tau total
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 2.906 3.788 0.1591 0.2235
## .25 .50 .75 .90 .95
## 0.5159 0.9164 3.6358 7.6384 8.4599
##
## lowest : 0.0909441 0.137882 0.162862 0.173781 0.244751
## highest: 7.40206 8.18975 8.21202 9.86446 25.2736
## --------------------------------------------------------------------------------
## UCHL-1
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 233.5 290.1 22.41 24.24
## .25 .50 .75 .90 .95
## 42.40 98.94 328.25 408.91 459.46
##
## lowest : 3.83908 15.6483 23.6062 23.885 24.3992
## highest: 408.195 410.577 429.266 630.565 2533.03
## --------------------------------------------------------------------------------
## Park7
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 135795 106138 28553 34804
## .25 .50 .75 .90 .95
## 68699 127422 168596 314497 340531
##
## lowest : 21580.4 28094.9 28634.1 34677.9 34857.5
## highest: 308420 328676 339356 347189 349055
## --------------------------------------------------------------------------------
## VCAM-1
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 1406175 1288257 307337 348707
## .25 .50 .75 .90 .95
## 558720 1017696 1603343 3799576 4341450
##
## lowest : 15554.9 295307 309460 327707 357707
## highest: 3779070 3847420 4304260 4552220 4839010
## --------------------------------------------------------------------------------
## VEGF
## n missing distinct Info Mean Gmd .05 .10
## 38 0 37 1 183.9 116.3 82.35 88.41
## .25 .50 .75 .90 .95
## 110.62 160.48 203.45 272.13 321.15
##
## lowest : 67.9363 76.8312 83.3222 85.4559 89.682
## highest: 271.961 272.513 298.801 447.804 923.274
## --------------------------------------------------------------------------------
## αSyn
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 1438 770.7 636.2 705.7
## .25 .50 .75 .90 .95
## 840.6 1128.7 2154.7 2336.8 2379.2
##
## lowest : 562.292 626.417 637.871 680.323 716.527
## highest: 2334.73 2341.67 2362.51 2473.96 2483.27
## --------------------------------------------------------------------------------
## Ang-1
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 28284 32352 1066 1089
## .25 .50 .75 .90 .95
## 1550 12721 51507 69674 78805
##
## lowest : 496.426 1013.14 1074.89 1085.18 1090.32
## highest: 66595.5 76855.7 77940.2 83703.7 84072
## --------------------------------------------------------------------------------
## Ang-2
## n missing distinct Info Mean Gmd .05 .10
## 38 0 36 1 3807 2726 1687 1776
## .25 .50 .75 .90 .95
## 2142 2999 3615 6883 9338
##
## lowest : 559.12 1408.85 1735.61 1793.46 1829.59
## highest: 6778.36 7126.48 9183.24 10212.2 17109.1
## --------------------------------------------------------------------------------
## APP
## n missing distinct Info Mean Gmd .05 .10
## 38 0 37 1 30928 24085 8244 10082
## .25 .50 .75 .90 .95
## 13634 23428 37387 53867 85948
##
## lowest : 7885.4 8153.22 8259.46 8679.6 10682.7
## highest: 53796.7 54030.8 82132.9 107566 111355
## --------------------------------------------------------------------------------
## BMP-9
## n missing distinct Info Mean Gmd .05 .10
## 38 0 34 0.999 113.8 144.6 2.314 2.886
## .25 .50 .75 .90 .95
## 3.563 37.683 204.781 333.845 365.306
##
## lowest : 1.48794 2.12933 2.34642 3.11682 3.22808
## highest: 327.321 349.067 362.144 383.222 419.644
## --------------------------------------------------------------------------------
## CaBD
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 1453 444.1 871.0 971.4
## .25 .50 .75 .90 .95
## 1156.1 1429.1 1802.9 1907.1 2018.1
##
## lowest : 770.303 860.826 872.843 942.805 983.6
## highest: 1890.42 1945.9 2008.5 2072.43 2120.95
## --------------------------------------------------------------------------------
## CCL2
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 674.7 885.4 67.89 93.50
## .25 .50 .75 .90 .95
## 132.34 293.69 562.38 1156.60 2096.66
##
## lowest : 56.919 60.936 69.1141 91.6015 94.3202
## highest: 1092.28 1306.68 2062.36 2291.03 8463.64
## --------------------------------------------------------------------------------
## CCL4
## n missing distinct Info Mean Gmd .05 .10
## 38 0 30 0.998 1169 1217 433.0 492.4
## .25 .50 .75 .90 .95
## 527.5 572.4 787.5 907.1 1459.8
##
## Value 400 450 500 550 600 700 750 800 850 900 1100
## Frequency 3 2 9 7 3 2 3 1 3 2 1
## Proportion 0.079 0.053 0.237 0.184 0.079 0.053 0.079 0.026 0.079 0.053 0.026
##
## Value 3450 17950
## Frequency 1 1
## Proportion 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 50
## --------------------------------------------------------------------------------
## CNTN1
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 12301 8370 2393 3398
## .25 .50 .75 .90 .95
## 6202 13723 18031 21212 22175
##
## lowest : 110.522 240.652 2772.58 3001.67 3567.66
## highest: 20982.7 21747.5 21790.2 24357.7 28555.5
## --------------------------------------------------------------------------------
## GM-CSF
## n missing distinct Info Mean Gmd .05 .10
## 38 0 26 0.998 58.43 78.01 14.05 15.61
## .25 .50 .75 .90 .95
## 17.99 23.40 27.39 31.66 38.91
##
## lowest : 10.6 13.443 14.1627 15.6117 16.3408
## highest: 31.4273 32.2023 38.8542 39.2488 1371.21
## --------------------------------------------------------------------------------
## ICAM-1
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 834610 421642 325324 428277
## .25 .50 .75 .90 .95
## 571495 827288 1116669 1342593 1395878
##
## lowest : 72927.6 256136 337534 370980 452833
## highest: 1331940 1367440 1393970 1406700 1566610
## --------------------------------------------------------------------------------
## IFNγ
## n missing distinct Info Mean Gmd .05 .10
## 38 0 23 0.995 122.3 52.82 83.85 86.78
## .25 .50 .75 .90 .95
## 95.78 101.98 110.16 147.29 208.88
##
## 71.0418353109784 (1, 0.026), 79.2535118340771 (1, 0.026), 83.3593500956264 (4,
## 0.105), 87.4651883571757 (1, 0.026), 91.5710266187251 (2, 0.053),
## 95.6768648802744 (6, 0.158), 99.7827031418238 (6, 0.158), 103.888541403373 (4,
## 0.105), 107.994379664922 (4, 0.105), 112.100217926472 (2, 0.053),
## 120.31189444957 (1, 0.026), 124.41773271112 (1, 0.026), 140.841085757317 (1,
## 0.026), 153.158600541965 (1, 0.026), 173.687791849712 (1, 0.026),
## 399.508896234925 (1, 0.026), 481.625661465912 (1, 0.026)
##
## For the frequency table, variable is rounded to the nearest 4.105838
## --------------------------------------------------------------------------------
## IL-10
## n missing distinct Info Mean Gmd .05 .10
## 38 0 30 0.998 53.07 75.4 6.354 6.848
## .25 .50 .75 .90 .95
## 7.995 8.660 48.181 153.730 190.433
##
## Value 4 6 8 10 12 16 26 28 44 46 48
## Frequency 1 12 8 1 1 1 1 1 1 1 1
## Proportion 0.026 0.316 0.211 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026
##
## Value 50 78 84 126 148 164 178 254 524
## Frequency 1 1 1 1 1 1 1 1 1
## Proportion 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 2
## --------------------------------------------------------------------------------
## IL-17
## n missing distinct Info Mean Gmd .05 .10
## 38 0 21 0.994 27.51 8.924 16.79 18.41
## .25 .50 .75 .90 .95
## 22.18 26.56 32.26 36.47 39.98
##
## lowest : 15.1749 16.0501 16.9255 17.801 18.6766
## highest: 36.2101 37.0875 39.7203 41.4758 55.5272
## --------------------------------------------------------------------------------
## IL-1α
## n missing distinct Info Mean Gmd .05 .10
## 38 0 35 0.999 53.22 25.91 27.45 31.10
## .25 .50 .75 .90 .95
## 35.96 47.16 66.84 89.82 96.11
##
## lowest : 15.0505 25.2321 27.8437 29.4365 31.807
## highest: 89.4051 90.804 95.335 100.535 102.09
## --------------------------------------------------------------------------------
## IL-2
## n missing distinct Info Mean Gmd .05 .10
## 38 0 24 0.995 52.41 45.63 12.13 17.44
## .25 .50 .75 .90 .95
## 23.22 34.82 64.37 134.33 146.58
##
## lowest : 7.82701 12.8851 16.2581 17.9449 21.3189
## highest: 132.804 137.876 146.33 148.02 170.004
## --------------------------------------------------------------------------------
## IL-4
## n missing distinct Info Mean Gmd .05 .10
## 38 0 25 0.996 167.4 26.92 130.4 142.9
## .25 .50 .75 .90 .95
## 156.2 165.1 177.3 191.6 228.5
##
## lowest : 115.203 118.168 132.597 140.973 143.723
## highest: 185.418 187.902 200.147 228.477 234.214
## --------------------------------------------------------------------------------
## IL-6
## n missing distinct Info Mean Gmd .05 .10
## 37 1 26 0.996 41.72 55.42 10.38 10.54
## .25 .50 .75 .90 .95
## 11.49 13.15 27.02 61.26 71.50
##
## lowest : 8.718 10.3787 10.6556 10.9325 11.4863
## highest: 57.5892 66.7718 70.3901 75.9579 758.135
## --------------------------------------------------------------------------------
## IL-8
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 1219 1690 29.46 56.00
## .25 .50 .75 .90 .95
## 187.19 305.34 969.25 4078.44 5213.84
##
## lowest : 16.2134 16.4467 31.7564 34.4507 65.2314
## highest: 3837.39 4640.89 5131.2 5682.16 6480.9
## --------------------------------------------------------------------------------
## IL-1β
## n missing distinct Info Mean Gmd .05 .10
## 38 0 28 0.998 208.5 361.5 21.42 21.94
## .25 .50 .75 .90 .95
## 23.63 29.79 45.15 66.76 124.91
##
## lowest : 17.4576 20.548 21.5771 22.0915 22.6057
## highest: 59.4334 83.8591 121.415 144.722 6494.61
## --------------------------------------------------------------------------------
## OPN
## n missing distinct Info Mean Gmd .05 .10
## 38 0 36 1 29434 44957 3803 4276
## .25 .50 .75 .90 .95
## 5025 7866 15264 24995 60000
##
## lowest : 2410.69 3235.79 3902.7 4086.45 4357.41
## highest: 23111.9 29389.9 57787 72540.9 650771
## --------------------------------------------------------------------------------
## PDGF AA
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 2323 1974 194.8 395.6
## .25 .50 .75 .90 .95
## 580.9 1790.7 3862.7 4281.8 4660.9
##
## lowest : 185.197 194.709 194.841 390.951 397.619
## highest: 4194.95 4484.38 4549.26 5293.59 5416.15
## --------------------------------------------------------------------------------
## RAGE
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 5832 2160 3449 3627
## .25 .50 .75 .90 .95
## 4609 5517 7194 8432 8995
##
## lowest : 2001.02 3032.51 3522.62 3544.69 3662.61
## highest: 8232.88 8897.76 8977.23 9096.55 9639.36
## --------------------------------------------------------------------------------
## TNF-α
## n missing distinct Info Mean Gmd .05 .10
## 38 0 26 0.997 34.09 40.65 11.03 12.04
## .25 .50 .75 .90 .95
## 13.29 14.55 17.92 23.35 46.37
##
## Value 9.5 11.0 12.0 12.5 13.0 13.5 14.0 14.5 15.0 16.0 17.5
## Frequency 2 1 3 3 2 5 1 5 2 2 3
## Proportion 0.053 0.026 0.079 0.079 0.053 0.132 0.026 0.132 0.053 0.053 0.079
##
## Value 19.0 19.5 20.0 22.5 25.0 42.0 68.5 652.0
## Frequency 1 1 2 1 1 1 1 1
## Proportion 0.026 0.026 0.053 0.026 0.026 0.026 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 0.5
## --------------------------------------------------------------------------------
## vWF
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 7748 5670 1838 2904
## .25 .50 .75 .90 .95
## 3952 6364 11388 16290 17664
##
## lowest : 603.296 687.787 2040.7 2782.31 2955.45
## highest: 16258 16364 17652.7 17730.6 18525.5
## --------------------------------------------------------------------------------
## Aβ(1-42)
## n missing distinct Info Mean Gmd .05 .10
## 38 0 17 0.948 2.252 3.136 0.2197 0.2197
## .25 .50 .75 .90 .95
## 0.2197 1.1581 1.9164 3.3664 5.8548
##
## Value 0.1933680 0.5168277 0.8402874 1.1637471 1.4872069 1.8106666
## Frequency 15 1 3 2 7 2
## Proportion 0.395 0.026 0.079 0.053 0.184 0.053
##
## Value 2.1341263 3.1045055 3.4279652 5.0452638 9.5736998 32.5393399
## Frequency 3 1 1 1 1 1
## Proportion 0.079 0.026 0.026 0.026 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 0.3234597
## --------------------------------------------------------------------------------
## BDNF
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 1075 1391 57.73 82.52
## .25 .50 .75 .90 .95
## 146.28 573.20 1475.78 2681.79 3978.30
##
## lowest : 2.3164 25.7357 63.371 70.8742 87.5161
## highest: 2376.89 3393.21 3673.41 5706.02 6223.19
## --------------------------------------------------------------------------------
## KLK6
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 1719 1120 485.7 600.2
## .25 .50 .75 .90 .95
## 873.5 1588.6 2345.4 2956.8 3245.9
##
## lowest : 36.9038 217.137 533.14 540.893 625.628
## highest: 2904.88 3077.92 3161.85 3722.45 3771.81
## --------------------------------------------------------------------------------
## MIF
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 545.9 326.4 133.6 154.1
## .25 .50 .75 .90 .95
## 326.7 576.3 713.9 924.6 1010.1
##
## lowest : 98.2922 114.485 137.001 150.099 155.794
## highest: 905.818 968.269 996.884 1085.15 1175.51
## --------------------------------------------------------------------------------
## NCAM-1
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 88282 33847 48803 61094
## .25 .50 .75 .90 .95
## 70018 82791 107151 129577 140988
##
## lowest : 9793.05 41135.8 50156.2 57708.7 62544.7
## highest: 129056 130793 140808 142005 155239
## --------------------------------------------------------------------------------
## NGF-β
## n missing distinct Info Mean Gmd .05 .10
## 36 2 23 0.994 2.42 2.608 0.3394 0.4358
## .25 .50 .75 .90 .95
## 0.9258 1.5288 2.5528 5.6670 8.7608
##
## lowest : 0.151001 0.339407 0.532259 0.62981 0.727961
## highest: 4.01429 7.31981 7.6434 12.1129 12.8833
## --------------------------------------------------------------------------------
## NRGN
## n missing distinct Info Mean Gmd .05 .10
## 37 1 28 0.997 388.3 608.8 6.69 15.42
## .25 .50 .75 .90 .95
## 24.42 43.73 385.17 1094.99 1961.83
##
## Value 0 20 40 60 80 120 200 240 380 460 560
## Frequency 5 13 4 1 1 1 1 1 1 1 1
## Proportion 0.135 0.351 0.108 0.027 0.027 0.027 0.027 0.027 0.027 0.027 0.027
##
## Value 800 980 1240 1780 2620 3280
## Frequency 2 1 1 1 1 1
## Proportion 0.054 0.027 0.027 0.027 0.027 0.027
##
## For the frequency table, variable is rounded to the nearest 20
## --------------------------------------------------------------------------------
## TDP-43
## n missing distinct Info Mean Gmd .05 .10
## 36 2 35 1 41943 54839 1224 1701
## .25 .50 .75 .90 .95
## 4843 8603 67199 126596 147669
##
## 0 (6, 0.167), 2000 (3, 0.083), 4000 (2, 0.056), 6000 (7, 0.194), 8000 (1,
## 0.028), 14000 (1, 0.028), 24000 (1, 0.028), 30000 (1, 0.028), 36000 (1, 0.028),
## 44000 (1, 0.028), 48000 (2, 0.056), 66000 (2, 0.056), 68000 (1, 0.028), 72000
## (1, 0.028), 120000 (1, 0.028), 122000 (1, 0.028), 130000 (1, 0.028), 142000 (1,
## 0.028), 158000 (1, 0.028), 214000 (1, 0.028)
##
## For the frequency table, variable is rounded to the nearest 2000
## --------------------------------------------------------------------------------
## YKL40
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 46817 30652 15947 17134
## .25 .50 .75 .90 .95
## 26161 41722 60201 72943 85597
##
## lowest : 10188.7 14008.1 16289.4 16461.4 17422.5
## highest: 71935.7 75294 80955.9 111898 159529
## --------------------------------------------------------------------------------
## Ang-2/Ang-1 ratio
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 0.9781 1.28 0.03617 0.04194
## .25 .50 .75 .90 .95
## 0.06606 0.49856 1.48002 1.87343 2.44431
##
## Value 0.00 0.05 0.10 0.45 0.55 0.60 0.95 1.10 1.15 1.20 1.30
## Frequency 7 7 4 2 1 1 1 1 1 1 1
## Proportion 0.184 0.184 0.105 0.053 0.026 0.026 0.026 0.026 0.026 0.026 0.026
##
## Value 1.45 1.55 1.65 1.70 2.15 2.30 2.95 9.05
## Frequency 2 1 1 3 1 1 1 1
## Proportion 0.053 0.026 0.026 0.079 0.026 0.026 0.026 0.026
##
## For the frequency table, variable is rounded to the nearest 0.05
## --------------------------------------------------------------------------------
## CCL18
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 81588 62176 8149 26083
## .25 .50 .75 .90 .95
## 47290 76752 95442 112587 164770
##
## lowest : 763.221 6996.59 8351.88 22232.5 27733.3
## highest: 110399 117692 152000 237135 418536
## --------------------------------------------------------------------------------
## CRP
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 669006 423672 210001 272911
## .25 .50 .75 .90 .95
## 446637 576268 747808 936719 1224757
##
## lowest : 86142 187534 213966 262556 277348
## highest: 895015 1034030 1114170 1851430 2922320
## --------------------------------------------------------------------------------
## NSE
## n missing distinct Info Mean Gmd .05 .10
## 35 3 25 0.997 1439805 2523330 38473 45664
## .25 .50 .75 .90 .95
## 50069 72502 954236 1638248 4854966
##
## 0 (9, 0.257), 50000 (12, 0.343), 150000 (2, 0.057), 250000 (1, 0.029), 8e+05
## (1, 0.029), 850000 (1, 0.029), 1e+06 (1, 0.029), 1100000 (1, 0.029), 1150000
## (1, 0.029), 1500000 (1, 0.029), 1550000 (1, 0.029), 1650000 (1, 0.029), 4250000
## (1, 0.029), 6150000 (1, 0.029), 28300000 (1, 0.029)
##
## For the frequency table, variable is rounded to the nearest 50000
## --------------------------------------------------------------------------------
## PDGF BB
## n missing distinct Info Mean Gmd .05 .10
## 37 1 36 1 10313 10404 580.5 676.3
## .25 .50 .75 .90 .95
## 1405.5 9639.1 18494.5 21719.8 25186.0
##
## lowest : 179.236 490.813 602.886 725.233 1006.73
## highest: 20624.9 23362.3 24671.4 27244.4 31458.5
## --------------------------------------------------------------------------------
## Fetuin A
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 167921152 36295598 110957073 126800493
## .25 .50 .75 .90 .95
## 150126663 168497529 189420188 204618271 219218301
##
## lowest : 93561700 110710000 111001000 121345000 129139000
## highest: 202523000 209508000 218996000 220477000 225856000
## --------------------------------------------------------------------------------
## CCL5
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 105115 106540 8071 8860
## .25 .50 .75 .90 .95
## 17678 89336 158612 233731 270829
##
## lowest : 4593.98 6928.17 8273.21 8465.71 9028.83
## highest: 229064 244620 261910 321370 340870
## --------------------------------------------------------------------------------
## IL-1RA
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 27955 27705 5007 5225
## .25 .50 .75 .90 .95
## 6772 24643 35599 53814 62624
##
## lowest : 3566.67 4686.05 5063.52 5158.19 5252.98
## highest: 53513.6 54514.2 55237.5 104478 146835
## --------------------------------------------------------------------------------
## MPO
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 2517185 1380762 760080 853461
## .25 .50 .75 .90 .95
## 1536309 2553248 3339946 4093459 4257109
##
## lowest : 563427 740996 763448 767231 890417
## highest: 4078600 4128130 4240650 4350370 5432490
## --------------------------------------------------------------------------------
## Lipocalin 2
## n missing distinct Info Mean Gmd .05 .10
## 38 0 38 1 560971 152631 311377 357476
## .25 .50 .75 .90 .95
## 493524 600060 659188 680091 717739
##
## lowest : 259243 291820 314829 346531 362167, highest: 679402 681700 705004 789906 858654
## --------------------------------------------------------------------------------
## Serpin E1
## n missing distinct Info Mean Gmd .05 .10
## 37 1 37 1 593106 568500 61600 109102
## .25 .50 .75 .90 .95
## 183488 347682 930071 1293283 1389225
##
## lowest : 44724.3 54937.8 63265.2 90599 121437
## highest: 1268240 1330850 1365640 1483550 2409590
## --------------------------------------------------------------------------------